A Frailty-Based Plasma Proteomic Signature Capturing Overall Health and Well-Being in Older Adults
Bibliographic record
Abstract
Abstract Frailty is an age-related syndrome characterized by an increased vulnerability to adverse health outcomes in the face of stressors. By deriving a blood-based proteomic signature for frailty, the current study aimed to enhance the understanding of frailty biology and derive a person-specific predictor for risk of frailty and other adverse age-related health outcomes. Penalized regression method was used to derive a 25-proteins signature (proteomic frailty index [pFI]) predictive of the cumulative frailty index (FI) in a training set of participants from the LonGenity cohort (N = 440). The pFI was validated with measured FI at baseline (r = 0.58) in the validation set (N = 440) from the same cohort and in two other independent cohorts: the Atherosclerosis Risk in Communities (ARIC) study (N = 5195, r = 0.61, p < 0.001) and the Baltimore Longitudinal Study of Aging (BLSA, N = 654, r = 0.45, p < 0.001). In all three cohorts, the pFI showed significant associations with age-related conditions including hypertension and diabetes, clinical measures of cholesterols, glucose and lipids, and functional measures of gait speed and grip strength (p < 0.001), after adjusting for demographic factors. In ARIC, the pFI were also significantly associated with cognitive scores (p < 0.001) and incident dementia (HR [95% CI] = 1.07 [1.05-1.09], follow-up: 7.22 [1.82] years). The pFI were also associated with mortality (LonGenity: HR [95% CI] = 1.12 [1.07-1.18]; ARIC: HR [95% CI] = 1.13 [1.12–1.14]; BLSA: HR [95% CI] = 1.11 [1.05–1.17]). In conclusion, we derived and validated a 25-protein signature of frailty that also captures overall well-being, health, and risk for key age-related diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".